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Record W4294842785 · doi:10.1080/00085006.2022.2106699

Zelens′kyi uses his communication skills as a weapon of war

2022· article· en· W4294842785 on OpenAlexaffvenue
Marta Dyczok, Yerin Chung

Bibliographic record

VenueCanadian Slavonic Papers · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and Russian Geopolitical Military Strategies
Canadian institutionsQueen's UniversityWestern University
Fundersnot available
KeywordsUkrainianDisinformationPolitical scienceFraming (construction)Media studiesNarrativeSociologyLawSocial mediaHistory

Abstract

fetched live from OpenAlex

Ukraine’s President Volodymyr Zelens′kyi’s communication skills have proven to be a powerful weapon against Russia’s disinformation war towards Ukraine. When Russia launched its full-scale military invasion of Ukraine in February 2022, he began recording daily messages to Ukrainian society and reaching out to international audiences through live addresses. This paper analyzes Zelens′kyi’s speeches during the first 50 days of the intensified war. It examines the agenda-setting and framing methods, honed by his television experience, that he used to reach audiences, as well as their content. It suggests that these speeches made Ukraine’s narrative dominant in international media, dispersing the information fog Russia was trying to create whereby Ukraine needed to be “de-Nazified,” neutralized, and kept in Russia’s sphere of influence. They also helped consolidate Ukrainian society and strengthen international assistance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.253
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2022
Admission routes2
Has abstractyes

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